Building material classification from Mapillary street-view images
In order to accurately predict the damage that a natural disaster would inflict on a building, detailed information on the building’s structure and composition material(s) is required. While all such information is virtually impossible to collect on a country level, since it would require complete, accessible and up-to-date cadaster records, an estimate of the building’s material can be obtained by simple visual inspection and is thus feasible to use.
Here we present a model to classify building material from street-level images of buildings, which enables quick extraction of this information on large areas. In particular, we used Mapillary, a service for sharing crowdsourced geotagged photos, as a source of street-level images.
As a starting point, we chose an area of about 50 km2 in the south-east of Blantyre, southern Malawi, in which a total of 90 km of roads have been captured, corresponding to about 13700 street-level images.
Mapillary employs computer vision models to detect specific objects in street-level images, among which buildings. For each image, these models provide:
- A list of objects detected in images
- The segment of the image corresponding to those objects
- A probability of the object being accurately detected
We thus selected only the images in which one or more buildings were detected with high accuracy (>80%) and good resolution (>1% of the picture). 3233 images satisfied the selection criteria in the study area. From these images, we selected only the area in which buildings were detected, by cropping each image around the corresponding segment(s).
We then randomly chose 1000 images and have them labeled. The labels referred to the buildings’ wall material and were chosen to be:
- Concrete
- Bricks
- Corrugated metal sheet
- Steel
- Glass
- Wood
- Thatch / grass
- Unclear / none of the above
Each image was labelled independently by 3 different people and we selected those images where 2 out of 3 labels agreed (889 out of 1000). We observe most of the buildings being labelled as either concrete or bricks. See this table for the full results:
| Label (Building Material) | Counts |
|---|---|
| Concrete | 394 |
| Bricks | 355 |
| Corrugated metal sheet | 25 |
| Steel | 5 |
| Glass | 11 |
| Wood | 12 |
| Thatch / grass | 3 |
| Unclear / none of the above | 84 |
Given the results of the labelling task, we develop a model to classify a building as either made of bricks or concrete; we do not have sufficient data to include other material classes. We start with a pre-trained Convolutional Neural Network (CNN), namely ResNet50, to extract high-level features from the images, and train a 2-layer fully-connected network on top of it. The parameters of the underlying ResNet50 model are kept fixed, i.e. the model is not re-trained. We use the keras framework for image pre-processing and model building/training.
- Install required packages:
pip install -r requirements.txt - Get image dataset and class labels (write to jmargutti@redcross.nl)
- Prepare dataset for the model:
prepare_dataset.py - Train and test the model:
train_test_model.py